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119 Table C. 1: Base Case Input -- Cereal Grains Cost and Time between the Chicago and New Orleans Metropolitan Areas by Waterway Cost for Path 1 ($/kilo-ton) Cost for Path 2 ($/kilo-ton) Time for Path 1 (hours) Time for Path 2 (hours) Average 52.43 53.81 442.50 449.25 Standard Deviation 1.65 1.66 0.46 0.46 Source: CMAP model Base Case Model Run Table C-2 illustrates the base case results for Cereal Grains transported from the Chicago area and the New Orleans area. The waterways are the dominant mode, accounting for 90 percent of the total tonnage and value moved, respectively. Truck is the second mode with a nine percent share of tonnage and value. Only a small fraction of the cereal grains is transported by rail. Truck movements are the secondary mode of transportation in the base case. Although trucks come with much faster delivery times, they do so at a much steeper price, and are generally used for short distances. However, trucks are an important part of the supply chain as they help consolidate grain shipments from farm to grain elevator or export terminals, and they also complement the waterways by bringing grain to ports. The input commodity flow information has limitations in the way this data is represented. Even with the above limitations, the model outputs were consistent with the findings from the industry outreach and from other research studies. Table C-2: Cereal Grain Tonnage and Value, Base Case (2007) Mode Tonnage (K-tons) Tonnage % Value ($M) Value % Water 1,396,429 90% 200,161,833 90% Rail 7,207 0% 1,034,400 0% Truck 146,311 9% 20,896,420 9% Air 0.256 0% 37 0% Total 1,549,947 100% 222,092,689 100% Source: CMAP model Disruption Case The disruption case simulates the movement of cereal grains between Chicago and New Orleans when waterway movements are not available. This scenario is not unrealistic. The effect of disruptions on the inland waterway system such as river closures and restrictions due to low water, high water, and lock maintenance or failures can lead to congestion, delays, spoilage, diversion to other transportation modes and ports, higher transportation costs, lost sales and lost market share.41 Shippers have several options under this scenario. If the waterways in Illinois are shut down, the first option preferred by shippers according to the interviews is to store the grain until the water movements become available again since this is the most cost-effective option. Modern grain elevators have improved their ability to store grain for long periods of time, allowing grain sellers to hold onto their product if they foresee a rise in market price. Another option is to dray the grain to another waterway, such as the Ohio River or through the Great Lakes. This method is 41 McGregor, Brian. A Reliable Waterway System is/ Important to Agriculture. February 2017. U.S. Dept. of Agriculture, Agricultural Marketing Service. Web. <http://dx.doi.org/10.9752/TS050.02-2017>
120 readily employed for droughts, floods and lock closures. Changing the port is easier than changing the mode of transportation or changing the buyer, which are the other two options available to grain sellers. In the case of bulk cereal grain shipments, the next most cost-effective option is by railroad. Several Class I railroads run north/south along the Mississippi River basin from Chicago to New Orleans. Both the waterways and the railroads provide efficiencies over long distances. Although the waterways have a cost advantage, the railroads compete on rates. The last option for the shipper is changing customers. Most of the grain in Illinois is corn, and corn can be used domestically for animal food production, ethanol production and many other food products. For our purposes, the only disruption scenario that can be modelled using the CMAP model is the shift of transportation modes. Storing the grain, changing the route, or finding a different buyer (destination) were not feasible due to the following reasons. First, the model does not model time-dependent trips. Second, modeling a route change requires trip assignment, choice set generation, and route choice modelling, which were beyond the scope of this task. Third, the origin and destination were fixed in the scope of the project. The CMAP model was manipulated to reflect the changes in cereal grain mode choice given disruptions to transportation modes. This corresponds to the hypothetical scenario above that assumes waterways are not available due to flood, drought or lock closure. To remove the waterways from the modal options available, the table of unit costs needs only to be adjusted upwards sufficiently to prevent waterway use. When doing so, it as expected that the next least expensive transportation mode (i.e., rail) would be selected. Disruption Case Setup and Results For the disruption scenario, we removed âinland waterwayâ as an option for the movement of grain (SCTG 2) originating from the study area and destined for New Orleans. The results of modelling the supply chain disruption for cereal grains matched the expected results. Error! Reference source not found.Table C-3 compares the scenario outputs of the base case with the disruption case. From this side by side comparison, we can see that the waterway movements shifted 100 percent from waterways to the railways. Considering how price sensitive the grain industry is to shipping costs, it was expected that the grain movements would switch to the next most affordable mode. Even through truck movements represented nine percent of the traffic in the base case, none of these truck trips shifted modes. This is expected as truck delivery is more expensive than both waterways and railways. Table C-3: Mode Paths used for Cereal Grains between Chicago and New Orleans, Percent Change between Base Case and Disruption Scenario Base Case (kilo-tons) % of Total Base Case Value % of Total Disruption Case (kilo- tons) % of Total Disruption Case Value % of Total Water 1,396,429 90% 200,161,833 90% - 0% - 0% Rail 7,207 0% 1,034,400 0% 1,403,636 91% 201,196,233 91% Truck 146,311 9% 20,896,420 9% 146,311 9% 20,896,420 9% Air 0.256 0% 37 0% 0.256 0% 37 0% Total 1,549,947 100% 222,092,689 100% 1,549,947 100% 222,092,689 100% Source: CMAP model
